Forecasting of Wind Induced Pressure on Setback Building Using Artificial Neural Network

Authors

  • Amlan Kumar Bairagi ORCID
    Affiliation

    Civil Engineering Department, Engineering Faculty, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, West Bengal, 711103, India

  • Sujit Kumar Dalui ORCID
    Affiliation

    Civil Engineering Department, Engineering Faculty, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, West Bengal, 711103, India

https://doi.org/10.3311/PPci.15769

Abstract

The wind load on an irregular plan shape tall building is quite different compared to a conventional plan shape tall building. Especially the aerodynamic parameters have extreme change due to the variety of setbacks at one or more the disparity of level. This paper highlights the prediction of pressure coefficient on square, single (20 %) setback and double (10 %) setback buildings for any wind incidence angle by CFD simulation and validated with Artificial Neural Network (ANN) and fast Fourier transform. The ANN is a widely used and efficient tool for different types of analyses. The 0° to 180° wind incidence angles (WIAs) considered as input data and respective face wise pressure coefficient (Cp) used as target data. The Levenberg-Marquardt training function and Mean Square Error (MSE) performance function used to train the target data. The face wise graphs of CFD, ANN and FFT are plotted in a single graph and the Cp of the surface checked by any random WIAs. Amazingly, the Cp of random WIA by ANN is almost similar to CFD. Furthermore, the error of ANN is 0.6 % to 2.5 %, which is negligible. According to this predicted graph, the design Cp of any WIA can be easily calculated and implement directly in the design.

Keywords:

pressure coefficient, drag, lift, artificial neural network (ANN), setback tall building

Published Online

2020-05-28

How to Cite

Bairagi, A. K., Dalui, S. K. “Forecasting of Wind Induced Pressure on Setback Building Using Artificial Neural Network”, Periodica Polytechnica Civil Engineering, 64(3), pp. 751–763, 2020. https://doi.org/10.3311/PPci.15769

Issue

Section

Research Article